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A novel centroid based sentence classification approach for extractive summarization of COVID-19 news reports.
Int J Inf Technol. 2023; 15(4):1789-1801.IJ

Abstract

A COVID-19 news covers subtopics like infections, deaths, the economy, jobs, and more. The proposed method generates a news summary based on the subtopics of a reader's interest. It extracts a centroid having the lexical pattern of the sentences on those subtopics by the frequently used words in them. The centroid is then used as a query in the vector space model (VSM) for sentence classification and extraction, producing a query focused summarization (QFS) of the documents. Three approaches, TF-IDF, word vector averaging, and auto-encoder are experimented to generate sentence embedding that are used in VSM. These embeddings are ranked depending on their similarities with the query embedding. A Novel approach has been introduced to find the value for the similarity parameter using a supervised technique to classify the sentences. Finally, the performance of the method has been assessed in two different ways. All the sentences of the dataset are considered together in the first assessment and in the second, each document wise group of sentences is considered separately using fivefold cross-validation. The proposed method has achieved a minimum of 0.60 to a maximum of 0.63 mean F1 scores with the three sentence encoding approaches on the test dataset.

Authors+Show Affiliations

Silchar, Assam 788010 India Computer Science and Engineering, National Institute of Technology Silchar.Silchar, Assam 788010 India Computer Science and Engineering, National Institute of Technology Silchar.Silchar, Assam 788010 India Computer Science and Engineering, National Institute of Technology Silchar.

Pub Type(s)

News

Language

eng

PubMed ID

37256024

Citation

Banerjee, Sumanta, et al. "A Novel Centroid Based Sentence Classification Approach for Extractive Summarization of COVID-19 News Reports." International Journal of Information Technology : an Official Journal of Bharati Vidyapeeth's Institute of Computer Applications and Management, vol. 15, no. 4, 2023, pp. 1789-1801.
Banerjee S, Mukherjee S, Bandyopadhyay S. A novel centroid based sentence classification approach for extractive summarization of COVID-19 news reports. Int J Inf Technol. 2023;15(4):1789-1801.
Banerjee, S., Mukherjee, S., & Bandyopadhyay, S. (2023). A novel centroid based sentence classification approach for extractive summarization of COVID-19 news reports. International Journal of Information Technology : an Official Journal of Bharati Vidyapeeth's Institute of Computer Applications and Management, 15(4), 1789-1801. https://doi.org/10.1007/s41870-023-01221-x
Banerjee S, Mukherjee S, Bandyopadhyay S. A Novel Centroid Based Sentence Classification Approach for Extractive Summarization of COVID-19 News Reports. Int J Inf Technol. 2023;15(4):1789-1801. PubMed PMID: 37256024.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A novel centroid based sentence classification approach for extractive summarization of COVID-19 news reports. AU - Banerjee,Sumanta, AU - Mukherjee,Shyamapada, AU - Bandyopadhyay,Sivaji, Y1 - 2023/03/24/ PY - 2022/8/17/received PY - 2023/2/28/accepted PY - 2023/5/31/medline PY - 2023/5/31/pubmed PY - 2023/5/31/entrez KW - Extractive text summarization KW - Query focused summarization KW - Sentence classification SP - 1789 EP - 1801 JF - International journal of information technology : an official journal of Bharati Vidyapeeth's Institute of Computer Applications and Management JO - Int J Inf Technol VL - 15 IS - 4 N2 - A COVID-19 news covers subtopics like infections, deaths, the economy, jobs, and more. The proposed method generates a news summary based on the subtopics of a reader's interest. It extracts a centroid having the lexical pattern of the sentences on those subtopics by the frequently used words in them. The centroid is then used as a query in the vector space model (VSM) for sentence classification and extraction, producing a query focused summarization (QFS) of the documents. Three approaches, TF-IDF, word vector averaging, and auto-encoder are experimented to generate sentence embedding that are used in VSM. These embeddings are ranked depending on their similarities with the query embedding. A Novel approach has been introduced to find the value for the similarity parameter using a supervised technique to classify the sentences. Finally, the performance of the method has been assessed in two different ways. All the sentences of the dataset are considered together in the first assessment and in the second, each document wise group of sentences is considered separately using fivefold cross-validation. The proposed method has achieved a minimum of 0.60 to a maximum of 0.63 mean F1 scores with the three sentence encoding approaches on the test dataset. SN - 2511-2112 UR - https://www.unboundmedicine.com/medline/citation/37256024/A_novel_centroid_based_sentence_classification_approach_for_extractive_summarization_of_COVID-19_news_reports. DB - PRIME DP - Unbound Medicine ER -
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